基于正交基的多视图迁移谱聚类

doi:10.19678/j.issn.1000-3428.0063091

摘要/Abstract

摘要： 挖掘多视图一致性是提升多视图聚类性能的关键，为更好地从多视图数据中学习一致性表示，提出一种新的多视图聚类算法OMTSC。OMTSC算法同时学习每个视图的聚类分配矩阵和特征嵌入，并将聚类分配矩阵分解为共享正交基矩阵和聚类编码矩阵。正交基矩阵可捕获并储存多视图一致性信息形成潜在聚类中心，经过加权融合的多视图聚类编码矩阵可更好地平衡不同视图的质量差异。引入基于二部图的协同聚类，实现正交基、聚类编码和特征嵌入3个矩阵的知识相互迁移，以提升多视图数据一致性和多样性，并利用特征嵌入的多样性最大化多视图一致性学习最优的潜在聚类中心，从而提高多视图聚类的性能。此外，基于群稀疏约束的特征嵌入可有效消除多视图数据中的噪声，提升算法的鲁棒性。在WikipediaArticles、COIL20和ORL数据集上的实验结果表明，与SC-Best、Co-Reg等先进的多视图聚类算法相比，OMTSC算法在ACC、NMI、ARI 3个评价指标上整体取得最优值，其中在COIL20和ORL数据集中的NMI评价指标均高于0.9。

关键词: 多视图, 正交基聚类, 迁移学习, 谱聚类, 协同正则化

Abstract: The consistency of multiview data is important for multiview clustering.To achieve multiview data with better consistency, this paper proposes a new multiview clustering algorithm, OMTSC.The OMTSC algorithm simultaneously learns the cluster assignment matrix and feature embedding of each view.Each cluster assignment matrix can be decomposed into shared orthogonal basis-cluster coding matrices.An orthogonal basis matrix can capture and store consistent multiview data and form latent cluster centers.A weighted multiview cluster coding matrix can balance the quality differences of different views effectively.Meanwhile, bipartite graph co-clustering is introduced to realize knowledge transfer, which involves clustering coding, feature embedding, and the orthogonal basis.This improves the multiview data consistency and diversity learning, as well as allows the OMTSC algorithm to leverage the diversity of feature embedding for maximizing multiview consistency and learning the optimal latent cluster centers, thus further improving the performance of multiview clustering.In addition, feature embedding based on group sparse constraints is robust to noise in view data.Experimental results on WikipediaArticles, COIL20, and ORL datasets show that the OMTSC algorithm is superior to SC-Best, Co-Reg, and advanced multiview clustering algorithms, and that it yields the highest score in all three evaluation indexes, i.e., the ACC, NMI, and ARI on COIL20 and ORL datasets, the NMI evaluation index for the OMTSC algorithm exceeds 0.9.

Key words: multiview, orthogonal basis clustering, transfer learning, spectral clustering, co-regularization

中图分类号:

TP18

王丽娟, 张霖, 尹明, 郝志峰, 蔡瑞初, 温雯. 基于正交基的多视图迁移谱聚类[J]. 计算机工程, 2022, 48(10): 37-44,54.

WANG Lijuan, ZHANG Lin, YIN Ming, HAO Zhifeng, CAI Ruichu, WEN Wen. Orthogonal Basis-Based Multiview Transfer Spectral Clustering[J]. Computer Engineering, 2022, 48(10): 37-44,54.

https://www.ecice06.com/CN/Y2022/V48/I10/37

图/表 5

参考文献

[1] NG A Y, JORDAN M I, WEISS Y.On spectral clustering:analysis and an algorithm[C]//Proceedings of NIPSʼ02.Cambridge, USA:MIP Press, 2002:849-856.
[2] ELHAMIFAR E, VIDAL R.Sparse subspace clustering:algorithm, theory, and applications[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11):2765-2781.
[3] LIU G C, LIN Z C, YAN S C, et al.Robust recovery of subspace structures by low-rank representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1):171-184.
[4] LI L M, CAI M L.Drug target prediction by multi-view low rank embedding[J].IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, 16(5):1712-1721.
[5] NIE F P, TIAN L, LI X L.Multiview clustering via adaptively weighted Procrustes[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York, USA:ACM Press, 2018:2022-2030.
[6] HU Z X, NIE F P, WANG R, et al.Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding[J].Information Fusion, 2020, 55:251-259.
[7] ZONG L L, ZHANG X C, LIU X Y, et al.Weighted multi-view spectral clustering based on spectral perturbation[C]//Proceedings of AAAI Conference on Artificial Intelligence.[S.1.]:AAAI Press, 2018:4621-4629.
[8] ZHAN K, ZHANG C Q, GUAN J P, et al.Graph learning for multiview clustering[J].IEEE Transactions on Cybernetics, 2018, 48(10):2887-2895.
[9] ZHAN K, NIE F P, WANG J, et al.Multiview consensus graph clustering[J].IEEE Transactions on Image Processing, 2019, 28(3):1261-1270.
[10] LIU J, JIANG Y, LI Z C, et al.Partially shared latent factor learning with multiview data[J].IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(6):1233-1246.
[11] PAN S J, YANG Q.A survey on transfer learning[J].IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10):1345-1359.
[12] 卢晨阳, 康雁, 杨成荣, 等.基于语义结构的迁移学习文本特征对齐算法[J].计算机工程, 2019, 45(5):116-121. LU C Y, KANG Y, YANG C R, et al.Text feature alignment algorithm for transfer learning based on semantic structure[J].Computer Engineering, 2019, 45(5):116-121.(in Chinese)
[13] 唐绍恩, 李骞, 胡磊, 等.一种基于迁移学习的能见度检测算法[J].计算机工程, 2019, 45(9):242-247. TANG S E, LI Q, HU L, et al.A visibility detection method based on transfer learning[J].Computer Engineering, 2019, 45(9):242-247.(in Chinese)
[14] BLITZER J, KAKADE S, FOSTER D.Domain adaptation with coupled subspaces[C]//Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. Washington D.C., USA:IEEE Press, 2011:173-181.
[15] BLUM A, MITCHELL T.Combining labeled and unlabeled data with co-training[C]//Proceedings of the 11th Annual Conference on Computational Learning Theory.Washington D.C., USA:IEEE Press, 1998:92-100.
[16] DE SA V R.Spectral clustering with two views[C]//Proceedings of International Conference on Machine Learning with Multiple Views.Berlin, Germany:Springer, 2005:20-27.
[17] HAN D, KIM J.Unsupervised simultaneous orthogonal basis clustering feature selection[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2015:5016-5023.
[18] HUANG Z Y, ZHOU J T, PENG X, et al.Multi-view spectral clustering network[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence.Macao, China:[s.n.], 2019:2563-2569.
[19] LI Y Q, NIE F P, HUANG H, et al.Large-scale multi-view spectral clustering via bipartite graph[C]//Proceedings of AAAI Conference on Artificial Intelligence.[S.1.]:AAAI Press, 2015, 29(1):2750-2756.
[20] SHI J B, MALIK J.Normalized cuts and image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8):888-905.
[21] LUXBURG U.A tutorial on spectral clustering[J].Statistics and Computing, 2007, 17(4):395-416.
[22] NIE F P, WANG X Q, HUANG H.Clustering and projected clustering with adaptive neighbors[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York, USA:ACM Press, 2014:977-986.
[23] KUMAR A, RAI P, DAUME H.Co-regularized multi-view spectral clustering[C]//Proceedings of NIPS'11.Cambridge, USA:MIP Press, 2011:1413-1421.
[24] HUANG H C, CHUANG Y Y, CHEN C S.Affinity aggregation for spectral clustering[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2012:773-780.
[25] ZHANG Z, LIU L, SHEN F M, et al.Binary multi-view clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(7):1774-1782.
[26] WEN J, ZHANG Z, ZHANG Z, et al.Generalized incomplete multiview clustering with flexible locality structure diffusion[J].IEEE Transactions on Cybernetics, 2021, 51(1):101-114.
[27] SAMARIA F S, HARTER A C.Parameterisation of a stochastic model for human face identification[C]//Proceedings of IEEE Workshop on Applications of Computer Vision.Washington D.C., USA:IEEE Press, 1994:138-142.
[28] NAYAR S.Columbia Object Image Library(COIL100)[EB/OL].[2021-09-20].https://www1.cs.columbia.edu/CAVE/publications/pdfs/Nene_TR96.pdf.
[29] XIA R K, PAN Y, DU L, et al.Robust multi-view spectral clustering via low-rank and sparse decomposition[C]//Proceedings of AAAI Conference on Artificial Intelligence.[S.1.]:AAAI Press, 2014:458-467.
[30] WEN Z W, GOLDFARB D, YIN W T.Alternating direction augmented Lagrangian methods for semidefinite programming[J].Mathematical Programming Computation, 2010, 2(3/4):203-230.
[31] LU C Y, FENG J S, YAN S C, et al.A unified alternating direction method of multipliers by majorization minimization[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(3):527-541.

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